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Scientific Machine Learning Jobs in Bryan, TX (NOW HIRING)

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Data Science Tutor

Bryan, TX ยท Remote

$40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery ... Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ...

Stay current on the latest advancements in machine learning, data science, and generative AI, evaluating their potential to solve the unique challenges the HPRC users face. Serve as the primary ...

Emphasizes readable, maintainable code and connects Python to machine learning, web scraping, scientific computing, and DevOps applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

Python Tutor

Bryan, TX ยท Remote

$40/hr

Emphasizes readable, maintainable code and connects Python to machine learning, web scraping, scientific computing, and DevOps applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

Linear Algebra Tutor

Bryan, TX ยท Remote

$40/hr

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... science to create personalized learning experiences. Through 1-on-1 Online Tutoring, students ...

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... science to create personalized learning experiences. Through 1-on-1 Online Tutoring, students ...

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Scientific Machine Learning information

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How much do scientific machine learning jobs pay per hour?

As of Jun 10, 2026, the average hourly pay for scientific machine learning in Bryan, TX is $29.02, according to ZipRecruiter salary data. Most workers in this role earn between $17.74 and $37.02 per hour, depending on experience, location, and employer.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in Scientific Machine Learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What are the key skills and qualifications needed to thrive as a Scientific Machine Learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What job categories do people searching Scientific Machine Learning jobs in Bryan, TX look for? The top searched job categories for Scientific Machine Learning jobs in Bryan, TX are:
What cities near Bryan, TX are hiring for Scientific Machine Learning jobs? Cities near Bryan, TX with the most Scientific Machine Learning job openings:
Machine Learning Engineer III

Machine Learning Engineer III

Electronic Arts Inc.

Washington, TX โ€ข On-site

$122K - $158K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 29 days ago


Job description

General Information
Locations: Kirkland, Washington, United States of America
  • Location: Kirkland
  • State: Washington
  • Country: United States of America

  • Location: Austin
  • State: Texas
  • Country: United States of America

Role ID
212201
Worker Type
Regular Employee
Studio/Department
CT - Security
Work Model
Hybrid
Description & Requirements
Electronic Arts creates next-level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story. Part of a community that connects across the globe. A place where creativity thrives, new perspectives are invited, and ideas matter. A team where everyone makes play happen.
Senior Machine Learning Engineer
The EA Security team makes sure that play is fair and protected, so it stays fun and exciting. We defend not only our players, but EA itself: our people's work, our games, our data, and our infrastructure. Powered by deep technical expertise, we use cutting-edge engineering, automation, and intelligence to tackle threats at global scale. Whether it's securing game services, combating fraud, or supporting fair play, we're at the frontline of protecting the future of interactive entertainment. Join us and help keep the world of play - and EA - secure.
The Senior Machine Learning Engineer will report to the Senior Manager, EA Player Security Data Labs. You will follow a hybrid work model with a mix of remote work and in-office collaboration. This role focuses on building and operating production-grade data and machine learning infrastructure that enables data scientists and analysts to deliver fraud detection, anti-cheat, and account security solutions across EA games.
Responsibilities
-Design, build, and maintain scalable data ingestion, transformation, and feature pipelines that support machine learning workflows for fraud and anti-cheat systems.
-Own and operate production data and machine learning infrastructure, including batch and near-real-time data processing, feature generation, training workflows, and inference pipelines.
-Partner with data scientists to productionize machine learning models, with a strong focus on data consistency, data quality, and reliable offline and online feature computation.
-Ensure data and machine learning pipelines are reliable, repeatable, observable, and cloud agnostic across environments.
-Contribute to architectural standards, platform design decisions, and engineering best practices as a senior individual contributor within EA Player Security Data Labs.
Qualifications
-Five or more years of professional experience in data engineering, machine learning engineering, or a closely related role with production ownership.
-Strong proficiency in Python and SQL, with demonstrated experience building and maintaining large-scale, production-grade data pipelines. Rust Experience a Plus.
-Experience designing and operating data-intensive systems using modern programming languages, including Rust.
-Hands-on experience supporting end-to-end machine learning workflows, with an emphasis on data preparation, feature pipelines, and model deployment infrastructure.
-Experience working in cloud environments such as AWS or GCP, including large-scale data processing systems.
-Experience with containerization and orchestration technologies such as Docker and Kubernetes.
-Experience with CI/CD systems and production deployment workflows, including GitLab.
-Experience with Terraform and Spark
Why You Will Enjoy This Role
You will work on data and machine learning systems that protect millions of players from fraud and cheating.
You will operate as a senior individual contributor with strong technical ownership and autonomy.
You will design and build core data infrastructure that powers machine learning across EA Player Security.
Why We Are Excited About This Role
We build systems that directly protect player trust and fair play across EA games.
We value strong data engineering fundamentals and production-focused machine learning.
We support senior engineers with autonomy and opportunities to shape long-term technical direction.
Pay Transparency - North America
COMPENSATION AND BENEFITS
The ranges listed below are what EA in good faith expects to pay applicants for this role in these locations at the time of this posting. If you reside in a different location, a recruiter will advise on the applicable range and benefits. Pay offered will be determined based on a number of relevant business and candidate factors (e.g. education, qualifications, certifications, experience, skills, geographic location, or business needs).
PAY RANGES
* Washington (depending on location e.g. Seattle vs. Spokane) *$122,300 - $158,500 USD
Pay is just one part of the overall compensation at EA.
In the US, we offer a package of benefits including paid time off (3 weeks per year to start), 80 hours per year of sick time, 16 paid company holidays per year, 10 weeks paid time off to bond with baby, medical/dental/vision insurance, life insurance, disability insurance, and 401(k) to regular full-time employees. Certain roles may also be eligible for bonus and equity.
About Electronic Arts
We're proud to have an extensive portfolio of games and experiences, locations around the world, and opportunities across EA. We value adaptability, resilience, creativity, and curiosity. From leadership that brings out your potential, to creating space for learning and experimenting, we empower you to do great work and pursue opportunities for growth.
We adopt a holistic approach to our benefits programs, emphasizing physical, emotional, financial, career, and community wellness to support a balanced life. Our packages are tailored to meet local needs and may include healthcare coverage, mental well-being support, retirement savings, paid time off, family leaves, complimentary games, and more. We nurture environments where our teams can always bring their best to what they do.
Electronic Arts is an equal opportunity employer. All employment decisions are made without regard to race, color, national origin, ancestry, sex, gender, gender identity or expression, sexual orientation, age, genetic information, religion, disability, medical condition, pregnancy, marital status, family status, veteran status, or any other characteristic protected by law. We will also consider employment qualified applicants with criminal records in accordance with applicable law. EA also makes workplace accommodations for qualified individuals with disabilities as required by applicable law.